Mlops

This skill provides comprehensive documentation and best practices for building production MLOps pipelines. It covers the full ML lifecycle including data preparation, model training, validation, and deployment strategies with guidance on using orchestration tools like Airflow, Dagster, and Kubeflow.

A comprehensive MLOps skill that provides specialized AI agents (data scientist, ML engineer, MLOps engineer) and workflow templates for building production machine learning pipelines. It guides users through data preparation, model training, validation, deployment, and monitoring stages using modern ML tools like MLflow, Kubeflow, and Feast.

Execute Hugging Face Hub operations using the hf CLI. Download models and datasets, upload files to Hub repositories, create repos, manage local cache, and run compute jobs on HF infrastructure. Supports authentication, file transfers, and cloud compute management.